Method and apparatus for automatic optimization of doppler imaging parameters

ABSTRACT

A method and apparatus are disclosed for automatic optimization of Doppler imaging parameters. The method includes obtaining and storing at least two characteristic spectral lines at a predetermined pulse repetition frequency. The method further includes optimizing at least one of the Doppler imaging parameters based on the stored at least two characteristic spectral lines and a predetermined mean noise power. In one embodiment, the characteristic spectral lines are obtained by collecting in real time the Doppler spectral lines generated at the predetermined pulse repetition frequency within a predetermined time period, and processing the collected Doppler spectral lines in real time without storing them.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese Patent ApplicationNo. 200710148460.9, entitled “Method and Apparatus for AutomaticOptimization of Doppler Imaging Parameters,” filed on Aug. 28, 2007,which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to ultrasound imaging, and moreparticularly, to a method and apparatus for automatic optimization ofDoppler imaging parameters.

SUMMARY OF THE INVENTION

One aspect of the present disclosure is a method for automaticoptimization of Doppler imaging parameters including at least one of apulse repetition frequency and a baseline. The method may includeobtaining and storing at least two characteristic spectral lines at apredetermined pulse repetition frequency. The method may also includeoptimizing at least one of the Doppler imaging parameters based on theat least two stored characteristic spectral lines and a predeterminedmean noise power. According to one embodiment, the at least twocharacteristic spectral lines are obtained by collecting in real timethe Doppler spectral lines generated at the predetermined pulserepetition frequency within a predetermined time period and processingthe collected Doppler spectral lines in real time without storing them.

Another aspect of the present disclosure is an apparatus for automaticoptimization of Doppler imaging parameters including at least one of apulse repetition frequency and a baseline. The apparatus may includemodules for obtaining and storing at least two characteristic spectrallines at a predetermined pulse repetition frequency. The apparatus mayfurther include a module for optimizing at least one of the Dopplerimaging parameters based on the at least two stored characteristicspectral lines and a predetermined mean noise power.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a typical system for optimization ofDoppler imaging parameters.

FIG. 2 is a Doppler spectrogram of carotid artery blood flow in a humanbody.

FIG. 3 is a flow diagram illustrating a method for automaticoptimization of Doppler imaging parameters according to the presentdisclosure.

FIG. 4 is a schematic diagram illustrating how to search for boundariesof a blood flow signal in the maximum power spectral line.

FIG. 5 is a Doppler spectrogram of FIG. 4 in which the pulse repetitionfrequency and the baseline has been adjusted.

FIG. 6 is a block diagram of an apparatus for automatic optimization ofDoppler imaging parameters according to the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Pulsed Wave (PW) Doppler technology is widely used for the losslessdetection and measurement of blood flow in a human body, by which onemay observe the blood flow characteristics of a particular area in ahuman body and obtain information about blood flow velocity anddistribution.

FIG. 1 is a block diagram of a typical system for optimization ofDoppler imaging parameters. At the beginning of the PW measurement, anoperator generally places a sampling gate at a position in atwo-dimensional ultrasound image of the patient to be measured. Anultrasound signal is then transmitted to the scanning position where thesampling gate is located. Scattering will occur when ultrasound signalsdirected into a human body encounter body cells. The signals will thenreach a receiving transducer, which converts sound signals intoelectrical signals. The electrical signals are subjected to low-noiseamplification, quadrature demodulation, and low-pass filtering to obtaintwo quadrature signals, i.e., in-phase (I) and quadrature (Q) signals. Asampling gate accumulation is performed for each of the quadraturesignals at the sampling gate position so as to obtain a complex-valuedsampling point of the Doppler signal at the current time instant.

Repeating the above-mentioned transmitting and receiving processes at acertain frequency, i.e., a Pulse Repetition Frequency (PRF), will obtainDoppler signals that vary with time. The resultant Doppler signals arewall-filtered to remove echo signals from tissues and vessel walls thathave rather low frequencies. In a given time period, the filteredDoppler signals in the sampling gate are fast-Fourier-transformed toobtain a power spectrum estimation of Doppler signals, therebyobtaining, based on the Doppler effect, the distribution of the bloodflow velocity in the sampling gate. Thus, the power spectrum that isvaried with time forms a Doppler spectrogram.

FIG. 2 shows a Doppler spectrogram of carotid artery blood flow in ahuman body. The abscissa represents time, and the ordinate representsfrequency (flow velocity). Upon Digital Scanning Conversion (DSC), theDoppler spectrogram is displayed. As shown in FIG. 1, if Doppler imagingparameters need to be optimized, a parameter adjusting unit istriggered. The parameter adjusting unit first obtains a Dopplerspectrogram (including Doppler spectral lines) for a certain timeperiod, which is analyzed to obtain the adjusted pulse repetitionfrequency and the adjusted baseline that will, in turn, be fed back to atransceiver unit and a spectrum analyzing unit, respectively.

When the pulse repetition frequency is lower than the Nyquist frequencyof the Doppler blood flow signal, or when the baseline position of theDoppler spectrogram is improperly set, the Doppler spectrogram will bealiased. In that case, the Doppler spectrogram will be wrapped around inthe velocity (frequency) scale direction such that the positive velocityvalues may appear negative and vice versa. If the total bandwidth of theDoppler spectrum is smaller than the pulse repetition frequency, asimple baseline shift will remove the aliasing (the aliasing in thiscase referred to as a single-aliasing). However, if the bandwidth of theDoppler spectrum is greater than the pulse repetition frequency, theDoppler spectrogram may not be effectively unwrapped by shifting thebaseline (the aliasing in this case referred to as a multi-aliasing).Instead, it is necessary to increase the pulse repetition frequency toexpand the velocity scale.

A conventional Doppler ultrasound diagnostic instrument generallyincludes, on the operational panel, two toggle switches for adjustingthe pulse repetition frequency and shifting the baseline. The switch istoggled to either increase or decrease the pulse repetition frequency orbaseline by a certain amount to reach the next level preset in thesystem. Though exhibiting good stability, such an operation is rathertime-consuming, especially when the difference between the current leveland the target level is relative large, because the operator is requiredto manually toggle the switch for multiple times to reach the desiredparameter value.

Techniques for automatically optimizing the pulse repetition frequencyand the baseline realize the adjustment by processing the Dopplerspectrogram for a certain time period to automatically determine theamplitude and position of the Doppler spectrogram.

U.S. Pat. No. 6,577,967 discloses one such technique, in which a Dopplerspectrogram is first collected in a given time period (including atleast one cardiac cycle), and then stored. To precisely determine theboundaries of the signal in the Doppler spectrogram, a noise model isprovided that uses a mean noise power of a system. The mean noise powerof the system is obtained by a summation of the estimated noise ofvarious portions of the system (including the preamplifier, ADquantization noise, etc.) at different gains. A noise threshold value isestimated based on the mean noise power of the system via the noisemodel. Thereafter, the Doppler spectrogram, which was collected in thegiven time period, is processed on a per-row basis, and the boundariesof the signal may be determined based on the processing characteristics.Finally, parameters such as the pulse repetition frequency and thebaseline etc. are adjusted on the basis of the boundaries as determined.

As will be understood by a person of skill in the art, the disclosedtechnique obtains the mean noise power by adding together the noise ofvarious portions, which is rather complicated. Moreover, a great deal ofcomputations and storage are required for storing all of the Dopplerspectral lines in at least one cardiac cycle and processing theseDoppler spectral lines on a per-row basis.

U.S. Pat. No. 6,663,566 discloses another technique, in which a Dopplerspectrogram is first obtained in a given time period (including at leastone cardiac cycle). All of the Doppler spectral lines for this timeperiod are averaged in each frequency bin, and a mean power spectralline is finally obtained. The minimum value of this mean power spectralline is then searched for. A determination is then made as to whetherthis minimum value is a blood flow signal by utilizing characteristicssuch as the variance and the mean value. If it is a blood flow signal,it indicates that the blood flow signal has spanned the entire frequencyaxis. In this case, parameter optimization will not be performed untilthe pulse repetition frequency is increased. Otherwise, it indicatesthat noise data are contained on the frequency axis, namely, the bloodflow signal has not spanned the entire frequency axis yet.

In this case, a noise threshold value is determined based on the minimumvalue of the mean power spectral line. With this noise threshold value,boundaries of the signal can be searched for in the mean power spectralline. The mean power spectral line is subsequently divided into a signalregion and a noise region according to the signal boundaries, and a newnoise threshold value is generated therefrom. A Doppler spectral linewith the largest intensity at the positive boundary of the signal of themean power spectral line is then determined from all of the Dopplerspectral lines in one cardiac cycle, and a boundary of that Dopplerspectral line is determined based on the new noise threshold value, theboundary being the positive boundary of the signal in theabove-mentioned one cardiac cycle.

Similarly, the negative boundary of the signal in one cardiac cycle maybe found out using the same procedure. With the positive and negativeboundaries of the signals in one cardiac cycle determined, parameterswill be capable of being automatically optimized.

The technique described in U.S. Pat. No. 6,663,566 precisely findspositive and negative boundaries of the signal in one cardiac cycle byadopting different procedures, which exhibits excellent stability.However, due to the heavy burden on computations and the great storagedemand, the method, as taught, is also not suitable for operating onequipment with limited resources (e.g., an embedded chip).

The method for automatic optimization of parameters described in U.S.Pat. No. 6,447,455 is primarily directed to parameters in the case ofsingle-aliasing. In particular, Doppler spectral lines are collected ina given time period, and energies on the positive and negative frequencyaxes of each of the Doppler spectral lines are summed up, and comparedwith one another. The larger one is considered as the direction of theDoppler spectrum. If the direction of the Doppler spectrum is positive,searching is made from the baseline upward. If the value of a frequencybin is larger than the threshold value, the peak index is updated;otherwise, it is not updated until the maximum value of the positivefrequencies is reached.

Subsequently, the search turns to the maximum value of the negativefrequencies and continues searching while updating the peak index untilit returns to the baseline position. If the final peak index is alsopositive, namely, in the same direction as the Doppler spectrum, it isconsidered that no single-aliasing occurs. On the contrary, asingle-aliasing is considered to have occurred. If the direction of theDoppler spectrum is negative, the searching is made from the baselinedownward using the same steps as described above. If the final peakindex is also negative, no single-aliasing occurs; otherwise, asingle-aliasing occurs. If a single-aliasing occurs, it is required toincrease the pulse repetition frequency.

Although the method described in the U.S. Pat. No. 6,447,455 is simplein theory, its algorithm tends to be compromised by noise interference.For example, when a speckle-like interfering signal affects the Dopplerspectral line, a misjudgment will occur. In addition, in the case wherethe Doppler spectrum is present above and below the baseline, thealgorithm is not reliable.

U.S. Patent Application No. 2007016073 introduces a method ofdetermining whether a multi-aliasing occurs in the Doppler spectrogramby matching templates. Given a great number of Doppler spectrogramtemplates, the algorithm as taught ensures a high accuracy. However,because of the multiplicity of Doppler spectrogram forms, it is requiredto collect an unduly large number of Doppler spectrogram templates. Inaddition, for some special Doppler spectrograms that do not exist in thetemplate base, the effect of the template matching is subjected touncertainty.

Accordingly, there is a need for a method and apparatus for automaticoptimization of Doppler imaging parameters, which has low requirementson system resources and exhibits excellent stability.

In order to reduce computational complexity and improve operability andstability, the present disclosure provides an improved method forautomatic optimization of Doppler imaging parameters including at leastone of a pulse repetition frequency and a baseline. In brief summary,the method may include obtaining and storing at least two characteristicspectral lines at a predetermined pulse repetition frequency. The methodmay further include optimizing at least one of the Doppler imagingparameters based on the at least two stored characteristic spectrallines and a predetermined mean noise power. In one embodiment, the atleast two characteristic spectral lines are obtained by collecting inreal time the Doppler spectral lines generated at the predeterminedpulse repetition frequency within a predetermined time period andprocessing the collected Doppler spectral lines in real time withoutstoring them.

In one embodiment, the predetermined pulse repetition frequency isselected from one of the system-allowable maximum pulse repetitionfrequency, the maximum pulse repetition frequency at a current detectiondepth, or the maximum pulse repetition frequency under a currentexamination mode.

In one embodiment, the at least two characteristic spectral linesinclude a maximum power spectral line and a mean power spectral line.

According to the present disclosure, obtaining at least twocharacteristic spectral lines may include: collecting in real time theDoppler spectral lines generated at the predetermined pulse repetitionfrequency; obtaining a final maximum power spectral line by selecting alarger power value in each frequency bin between the currently storedmaximum power spectral line and the currently collected Doppler spectralline within the predetermined time period; obtaining a total powerspectral line by accumulating in real time a power value in eachfrequency bin of the Doppler spectral lines collected in real timewithin the predetermined time period; and obtaining the mean powerspectral line by dividing the total power spectral line by the number ofthe Doppler spectral lines collected in real time within thepredetermined time period.

According to the present disclosure, optimizing at least one of theDoppler imaging parameters may include: filtering out the power valuesof the maximum power spectral line and the mean power spectral linewithin the frequency cutoff range of wall filtering, thereby obtaining anew maximum power spectral line and a new mean power spectral line;determining the frequency bin corresponding to the minimum power valueof the new maximum power spectral line; deciding whether or not amulti-aliasing occurs by comparing the power value of the new mean powerspectral line corresponding to the determined frequency bin with a firstthreshold value, wherein if the power value of the new mean powerspectral line corresponding to the determined frequency bin is largerthan the first threshold value, it is determined that the multi-aliasingoccurs; otherwise, no multi-aliasing occurs; searching for boundaries ofa blood flow signal based on the new maximum power spectral line and thepredetermined mean noise power if no multi-aliasing occurs, theboundaries of the blood flow signal including a positive frequencyboundary and a negative frequency boundary; adjusting the pulserepetition frequency down to the minimum value and the baseline to thecenter of a display area if no boundaries of the blood flow signal arefound; and adjusting at least one of the Doppler imaging parametersbased on the boundaries of the blood flow signal if the boundaries ofthe blood flow signal are found.

In one embodiment, the first threshold value depends on thepredetermined mean noise power.

According to the present disclosure, searching for boundaries of a bloodflow signal includes: searching for a negative frequency boundary of theblood flow signal in the new maximum power spectral line, which furthercomprises: searching from the determined frequency bin in a positivefrequency direction; if the maximum power values in a number ofconsecutive frequency bins are larger than a second threshold value,determining that the negative frequency boundary of the blood flowsignal is found; and, if not, continuing searching in the positivefrequency direction until the maximum frequency bin and then searchingfrom the minimum frequency bin up to the determined frequency bin.Searching for boundaries of a blood flow signal further comprises:searching for a positive frequency boundary of the blood flow signal inthe new maximum power spectral line, which further comprises: searchingfrom the determined frequency bin in a negative frequency direction; ifthe maximum power values in a number of consecutive frequency bins arelarger than the second threshold value, determining that the positivefrequency boundary of the blood flow signal is found; and, if not,continuing searching in the negative frequency direction until theminimum frequency bin and then searching from the maximum frequency bindown to the determined frequency bin.

In one embodiment, the second threshold value depends on thepredetermined mean noise power.

In one embodiment, adjusting at least one of the Doppler imagingparameters based on the boundaries of the blood flow signal comprisesadjusting the pulse repetition frequency and the baseline based on thefollowing equations:

$\begin{matrix}{{P\; R\; F_{new}} = {{P\; R\;{F_{old}/\left( \frac{k*N}{N - \left( {f_{-} - f_{+}} \right)} \right)}} = {P\; R\;{F_{old}/L}}}} & {{Eq}.\mspace{14mu} 1} \\{{baseline}_{new} = {{\frac{1 - k}{2}*N} + {\left( {{baseline}_{old} - f_{-}} \right)*L}}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

where, PRF_(old) and PRF_(new) are the pulse repetition frequency andthe corresponding adjusted pulse repetition frequency, respectively;baseline_(old) and baseline_(new) are the baseline and the correspondingadjusted baseline, respectively; N is the number of frequency bins ofthe Doppler spectral lines; k is a predefined constant between 0 and 1,indicating the desired ratio of the frequency range of the blood flowsignal to the entire frequency space; f⁻ is the negative frequencyboundary of the blood flow signal; f₊ is the positive frequency boundaryof the blood flow signal; and L is the ratio of the pulse repetitionfrequency to the corresponding adjusted pulse repetition frequency.

In one embodiment, the predetermined time period is equal to at leastone cardiac cycle.

In one embodiment, the predetermined mean noise power is measured inadvance at different detection depths under different sampling gatewidth conditions.

The present disclosure also provides an apparatus for automaticoptimization of Doppler imaging parameters including at least one of apulse repetition frequency and a baseline. The apparatus may include: anobtaining module for obtaining at least two characteristic spectrallines at a predetermined pulse repetition frequency; a storage modulefor storing the at least two characteristic spectral lines; and anoptimizing module for optimizing at least one of the Doppler imagingparameters based on the stored at least two characteristic spectrallines and a predetermined mean noise power, wherein the obtaining moduleobtains at least two characteristic spectral lines by collecting in realtime the Doppler spectral lines generated at the predetermined pulserepetition frequency within a predetermined time period, and processingthe collected Doppler spectral lines in real time without storing them.

In one embodiment, the predetermined pulse repetition frequency isselected from one of the system-allowable maximum pulse repetitionfrequency, the maximum pulse repetition frequency at a current detectiondepth, or the maximum pulse repetition frequency under a currentexamination mode.

In one embodiment, the at least two characteristic spectral linesinclude a maximum power spectral line and a mean power spectral line.

According to the present disclosure, the obtaining module may include: acollecting module for collecting in real time the Doppler spectral linesgenerated at the predetermined pulse repetition frequency; a maximumpower spectral line obtaining module for obtaining a final maximum powerspectral line by selecting a larger power value in each frequency binbetween the currently stored maximum power spectral line and thecurrently collected Doppler spectral line within the predetermined timeperiod; a total power spectral line obtaining module for obtaining atotal power spectral line by accumulating in real time a power value ineach frequency bin of the Doppler spectral lines collected in real timewithin the predetermined time period; and a mean power spectral lineobtaining module for obtaining the mean power spectral line by dividingthe total power spectral line by the number of the Doppler spectrallines collected in real time within the predetermined time period.

According to the present disclosure, the optimizing module may include:a filtering-out module for filtering out the power values of the maximumpower spectral line and the mean power spectral line within thefrequency cutoff range of wall filtering, thereby obtaining a newmaximum power spectral line and a new mean power spectral line; adetermining module for determining the frequency bin corresponding tothe minimum power value of the new maximum power spectral line; analiasing deciding module for deciding whether or not a multi-aliasingoccurs by comparing the power value of the new mean power spectral linecorresponding to the determined frequency bin with a first thresholdvalue, wherein if the power value of the new mean power spectral linecorresponding to the determined frequency bin is larger than the firstthreshold value, it is determined that the multi-aliasing occurs;otherwise, no multi-aliasing occurs; a boundary searching module forsearching for boundaries of a blood flow signal based on the new maximumpower spectral line and the predetermined mean noise power if nomulti-aliasing occurs, the boundaries of the blood flow signal includinga positive frequency boundary and a negative frequency boundary; and anadjusting module for adjusting the pulse repetition frequency down tothe minimum value and the baseline to the center of a display area if noboundaries of the blood flow signal are found and adjusting at least oneof the Doppler imaging parameters based on the boundaries of the bloodflow signal if the boundaries of the blood flow signal are found.

In one embodiment, the first threshold value depends on thepredetermined mean noise power.

In one embodiment, the predetermined time period is equal to at leastone cardiac cycle.

In one embodiment, the predetermined mean noise power is measured inadvance at different detection depths under different sampling gatewidth conditions.

It is generally required in conventional systems to store all of theDoppler spectral lines generated within a time period (including atleast one cardiac cycle), which places a heavy burden on the memory. Inaddition, it is also generally required in conventional systems toanalyze the stored Doppler spectral lines, thus involving a large numberof computations. Therefore, optimizing Doppler imaging imposes highrequirements on system resources in conventional systems. In contrast tothe prior art, a method and apparatus according to the presentdisclosure may implement the automatic optimization of Doppler imagingparameters by storing and processing two characteristic spectral linesand a predetermined mean noise power, thereby requiring less resourcesand exhibiting low complexity and good stability.

Referring now to FIG. 3, there is shown a flowchart of a method forautomatic optimization of Doppler imaging parameters according to oneembodiment of the disclosure. The method starts at step 300 and ends atstep 360. To eliminate the multi-aliasing as much as possible, at step305, the pulse repetition frequency is set to the maximum pulserepetition frequency at a current detection depth, which is thenprovided to an ultrasound transmitting unit. Assuming d is the currentdetection depth, and c is the speed at which the ultrasound wavepropagates in a human body, the maximum pulse repetition frequency atthe current detection depth may be defined as:

$\begin{matrix}{{P\; R\; F_{\max}} = \frac{c}{2*d}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

With the pulse repetition frequency set to PRF_(max), because the rangeof blood flow velocity (blood flow Doppler frequency) becomes wider,and, in most cases, the multi-aliasing that occurs in the Dopplerspectrogram can be eliminated. Unfortunately, when the blood flow passesthrough the position to be detected at a rather high velocity, themulti-aliasing may still exist.

The pulse repetition frequency may also be set to the system-allowablemaximum pulse repetition frequency. The system-allowable maximum pulserepetition frequency at the current detection depth may exceedPRF_(max). If that occurs, the system may be switched to a high pulserepetition frequency (HPRF) operation mode. Moreover, the pulserepetition frequency may also be set based on the examination mode. Forexample, in the mode adaptable for examination of a small organ, sincethe blood flow flows at a low velocity, the pulse repetition frequencymay thus be set to a relatively small value.

At step 310, Doppler spectral lines generated by a frequency spectrumestimating unit within one second (assuming that the heart rate of aperson is generally larger than 60 beats/min, one second is longer thanone cardiac cycle) are collected and processed in real time to obtaintwo characteristic spectral lines, i.e. the maximum power spectral lineand the mean power spectral line. The number of Doppler spectral linesgenerated within one second is dependent on the spectrum update rate,which may be adjusted by a user via a button on the front panel of thesystem. Assuming that the spectrum is updated at a rate of SpecSpeed ms,the number of the Doppler spectral lines generated within one second maybe:

$\begin{matrix}{M = \frac{1000}{SpecSpeed}} & {{Eq}.\mspace{14mu} 4}\end{matrix}$

Each spectral line has N frequency bins numbered as 0 to N−1 from bottomto top, each representing a different blood flow velocity. N correspondsto the number of Fast Fourier Transform (FFT) points.

The maximum power spectral line may be generated as follows. The powervalue in a certain frequency bin of the Doppler spectral line generatedat the current time instant is compared with the power value in thecorresponding frequency bin of the stored maximum power spectral lineand the larger power value is used as the new power value in thecorresponding frequency bin of the maximum power spectral line. The sameoperations are performed for each frequency bin to obtain the maximumpower spectral line at the current time instant, which is then stored inthe memory. Each of the Doppler spectral lines generated within onesecond may be processed in the same way, thereby obtaining the finalmaximum power spectral line, indicated by MX. In one embodiment, theinitial power value in each frequency bin of the maximum power spectralline is always 0.

The mean power spectral line may be generated as follows. Add the powervalue in a certain frequency bin of the Doppler spectral line generatedat the current time instant to the power value in the correspondingfrequency bin of the stored total power spectral line and the resultantsum is used as the new power value in the corresponding frequency bin ofthe total power spectral line. The same operations are performed foreach frequency bin to obtain the total power spectral line at thecurrent time instant, which is then stored in the memory. Each of theDoppler spectral lines generated within one second may be processed inthe same way, thereby obtaining the final total power spectral line. Thetotal power spectral line is divided by the number of the Dopplerspectral lines generated within one second to obtain the mean powerspectral line, indicated by MN. In one embodiment, the initial powervalue in each frequency bin of the total power spectral line is always0.

The maximum power spectral line and the mean power spectral line havebeen obtained as described above. All of the subsequent operations willbe performed on these two characteristic spectral lines. To reduce theeffect of the low-frequency, high-intensity power values caused byechoes from vessel walls and tissues on the optimization of Dopplerimaging parameters, according to the setting of the cutoff frequency ofwall filtering, the power values within the frequency cutoff range ofwall filtering, which will be ignored in the subsequent processing, areset invalid in the maximum power spectral line and the mean powerspectral line.

At step 320, a decision is made as to whether or not a multi-aliasingoccurs in the Doppler spectrogram. At first, in order to search for thefrequency bin corresponding to the minimum power value of the maximumpower spectral line MX, referred to herein as “the minimum powerfrequency bin,” the initial value of the minimum power frequency bin isset to 0, and then the initial minimum power value is set to the powervalue in the frequency bin numbered as 0 of the maximum power spectralline. Thereafter, beginning with the frequency bin numbered as 1, thepower value in each of the frequency bins of the maximum power spectralline is in turn compared with the current minimum power value until thefrequency bin numbered as N−1, the smaller one being used as the newminimum power value, and the corresponding frequency bin as the newminimum power frequency bin. The final minimum power frequency bin isregarded as the minimum power frequency bin of the maximum powerspectral line, indicated by f_(min). Next, it is decided whether thefollowing equation is true or false:MN _(f) _(min) <C1  Eq. 5

Where MN_(f) _(min) refers to the value of the mean power spectral lineMN at f_(min), and C1 refers to a threshold value that is set based on apredetermined mean noise power. C1 may be, for example, two times thepredetermined mean noise power. The predetermined mean noise power maybe obtained by measuring the mean power of the Doppler spectrogram whilethe probe is idle. If Eq. 5 is false, namely, the power of the meanpower spectral line at f_(min) is larger than C1, the signal isconsidered to present at f_(min). That is, the signal has spanned theentire frequency axis of the Doppler spectrogram, in which themulti-aliasing occurs. On the contrary, if Eq. 5 is true, namely, thepower of the mean power spectral line at f_(min) is smaller than C1, thenoise is considered to present at f_(min). That is, the signal has notspanned the entire frequency axis of the Doppler spectrogram, in whichno multi-aliasing occurs.

If a multi-aliasing occurs in the Doppler spectrogram at step 325, themethod according to the present disclosure proceeds to step 340. At thisstep, the original pulse repetition frequency and the baseline remainunchanged, because the pulse repetition frequency at the moment isalready the maximum pulse repetition frequency at the current detectiondepth. It is impossible to remove the multi-aliasing by increasing thepulse repetition frequency.

If there is no occurrence of the multi-aliasing in the Dopplerspectrogram at step 325, the method proceeds to step 330. At this step,boundaries of the blood flow signal are searched for in the maximumpower spectral line, wherein the boundaries of the blood flow signalincludes a positive frequency boundary and a negative frequencyboundary. This will be described hereafter in greater detail.

At first, a new noise threshold value C2 is determined based on thepredetermined mean noise power. Then, the negative frequency boundary ofthe blood flow signal is searched for in the maximum power spectral lineMX. C2 may be, for example, 10 times the predetermined mean noise power.

FIG. 4 shows a schematic diagram illustrating how to search for theboundaries of the blood flow signal in the maximum power spectral line.In the maximum power spectral line MX, the searching starts from f_(min)upward. If the maximum power values in consecutive C3 frequency bins arelarger than the above new noise threshold value C2, the negativefrequency boundary f⁻ of the blood flow signal is considered to befound. Otherwise, the searching continues upward until the frequency binnumbered as N−1. Thereafter, the searching is carried on from thefrequency bin numbered as 0 up to f_(min).

The approach for searching for the positive frequency boundary issimilar to that for searching for the negative frequency boundary,except that the searching starts from f_(min) downward. After thesearching reaches the frequency bin numbered as 0, it continues from thefrequency bin numbered as N−1 down to f_(min). The positive frequencyboundary of the blood flow signal is indicated as f₊. C3 represents thediscernable minimum frequency range of the signal. The smaller C3 is,the smaller the discernable frequency range of the signal is and thehigher the sensitivity is. In one embodiment, the value of C3 should bedetermined in accordance with circumstances to achieve a balance betweenthe sensitivity and the robustness of the algorithm. For example, inthis embodiment, the value of C3 is 10. In practical applications, C3may be a positive integer less than N, as required.

Returning to FIG. 3, if no boundaries of the blood flow signal can befound at step 335, the frequency range of the blood flow signal in theDoppler spectrogram is too small. The display range of the flow velocityis therefore decreased in order to correctly display the blood flowsignal. Hence, the method goes to step 350, where the pulse repetitionfrequency is adjusted down to the minimum value in the current detectionmode, and the baseline is adjusted to the center of the display area.

If the boundaries of the blood flow signal are found at step 335, themethod goes to step 345, where the pulse repetition frequency and thebaseline are adjusted based on the positions of f₊ and f⁻. This will bedescried in details below.

The process for adjusting the pulse repetition frequency is described asfollows. At first, the region where the blood flow signal is distributedis delimited in the spectrogram, and then a ratio of the region asdelimited to the entire frequency space is calculated. Finally, a newpulse repetition frequency is calculated based on said ratio and adesired scaling ratio.

From the maximum power spectral line in the Doppler spectrogram shown inFIG. 4, it can be seen that the signal region is given by the equationRos=N−(f ⁻ −f ₊)  Eq. 6

where Ros represents the signal region, and N represents the number ofthe frequency bins of the Doppler spectral lines, i.e., the number ofFFT points.

The ratio of the signal region to the entire frequency space is

$\frac{Ros}{N}.$If the desired scaling ratio is k, the new pulse repetition frequency iscalculated in accordance with:

$\begin{matrix}{{P\; R\; F_{new}} = {{P\; R\;{F_{old}/\left( \frac{k*N}{Ros} \right)}} = {P\; R\;{F_{old}/L}}}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

where PRF_(old) and PRF_(new) are the pulse repetition frequency and thecorresponding adjusted pulse repetition frequency, respectively; k is apredefined constant between 0 and 1, indicating the desired ratio of thefrequency range of the blood flow signal to the entire frequency space;and L represents a ratio of the pulse repetition frequency to thecorresponding adjusted pulse repetition frequency. As the pulserepetition frequency of an ultrasound system is a discrete value, thecalculated PRF_(new) should be matched, in one embodiment, with thepulse repetition frequency allowed by the system. The minimum value ofall values that are larger than PRF_(new) may be determined from thediscrete pulse repetition frequency values and used as the adjustedpulse repetition frequency.

The position of the baseline may be optimized based on the positions off₊ and f⁻, as described below. With reference to FIG. 4, the position ofthe adjusted base line may be defined by:

$\begin{matrix}{{baseline}_{new} = {{\frac{1 - k}{2}*N} + {\left( {{baseline}_{old} - f_{-}} \right)*L}}} & {{Eq}.\mspace{14mu} 8}\end{matrix}$

where baseline_(old) and baseline_(new) are the baseline and thecorresponding adjusted baseline, respectively. The other parameters aredefined above. Likewise, the baseline position in the ultrasound systemis determined based on discrete values. Therefore, the calculatedbaseline_(new) should be compared with possible baseline values in thesystem one by one, in order to find out the baseline value closest toit, which is then used as the new baseline.

Shown in FIG. 5 is the spectrogram of FIG. 4 with the pulse repetitionfrequency and the baseline adjusted according to the techniques of thepresent disclosure. It can be seen from FIG. 5 that no aliasing existsin the Doppler spectrogram with the related parameters adjusted, andthat the frequency range of the signal takes up a suitable proportion ofthe entire frequency space.

FIG. 6 is a block diagram of an apparatus 600 for automatic optimizationof Doppler imaging parameters according to the present disclosure. Asshown in FIG. 6, the apparatus 600 comprises an obtaining module 610, astorage module 635 and an optimizing module 640. According to anembodiment of the present disclosure, to implement the automaticoptimization of Doppler imaging parameters, the ultrasound transmittingunit in the ultrasound system transmits ultrasound waves according toPRF_(max). The obtaining module 610 obtains a maximum power spectralline and a mean power spectral line by collecting in real time theDoppler spectral lines generated by the frequency spectrum estimatingunit within one second, and processing the collected Doppler spectrallines in real time without storing them. The storage module 635 storesthe maximum power spectral line and the mean power spectral line. Theoptimizing module 640 optimizes at least one of the Doppler imagingparameters based on the stored maximum power spectral line, the meanpower spectral line, and the predetermined mean noise power. Thepredetermined mean noise power is measured in advance at differentdetection depths under different sampling gate width conditions.

According to an embodiment of the present disclosure, the obtainingmodule 610 comprises a collecting module 615, a maximum power spectralline obtaining module 620, a total power spectral line obtaining module625 and a mean power spectral line obtaining module 630. The collectingmodule 615 collects in real time the Doppler spectral lines generated bythe frequency spectrum estimating unit. The maximum power spectral lineobtaining module 620 obtains a final maximum power spectral line byselecting a larger power value in each frequency bin between thecurrently stored maximum power spectral line and the currently collectedDoppler spectral line within one second. The total power spectral lineobtaining module 625 obtains a total power spectral line by accumulatingin real time a power value in each frequency bin of the Doppler spectrallines collected in real time within one second, and provides the totalpower spectral line to the mean power spectral line obtaining module630. The mean power spectral line obtaining module 630 obtains a meanpower spectral line by dividing the total power spectral line by thenumber of the Doppler spectral lines collected in real time within onesecond.

In one embodiment, the optimizing module 640 comprises a filtering-outmodule 645, a determining module 650, an aliasing deciding module 655, aboundary searching module 660 and an adjusting module 665. Thefiltering-out module 645 filters out the power values of the storedmaximum power spectral line and mean power spectral line within thefrequency cutoff range of wall filtering, thereby obtaining the newmaximum power spectral line and the new mean power spectral line. Thedetermining module 650 determines the frequency bin f_(min)corresponding to the minimum power value of the new maximum powerspectral line. The aliasing deciding module 655 decides whether or not amulti-aliasing occurs by comparing the power value of the new mean powerspectral line at f_(min) with C1. If the power value of the new meanpower spectral line at f_(min) is larger than C1, a multi-aliasing isdetermined to have occurred. Otherwise, there is no occurrence of amulti-aliasing. In case there is no occurrence of a multi-aliasing, theboundary searching module 660 searches for the boundaries of the bloodflow signal based on the new maximum power spectral line and thepredetermined mean noise power. The adjusting module 665 adjusts theDoppler imaging parameters based on the output of the boundary searchingmodule 660. When the boundaries of the blood flow signal can not befound, the pulse repetition frequency will be adjusted down to theminimum value, and the baseline will be adjusted to the center of thedisplay area. On the contrary, when the boundaries of the blood flowsignal are found, at least one of the Doppler imaging parameters will beadjusted based on the boundaries of the blood flow signal.

The method and apparatus according to the present disclosure has beenproven feasible in the color Doppler diagnostic system, which isapplicable not only to the real time optimization of Doppler imagingparameters, but also to the offline optimization of Doppler imagingparameters.

Detailed descriptions of several example embodiments are provided above.However, the invention is not restricted to these example embodiments.Without departing from the scope of the invention, those skilled in thisart may make changes and modifications, which will all fall into theclaims of the invention.

Furthermore, the described features, operations, or characteristics maybe combined in any suitable manner in one or more embodiments. It willalso be readily understood that the order of the steps or actions of themethods described in connection with the embodiments disclosed may bechanged as would be apparent to those skilled in the art. Thus, anyorder in the drawings or Detailed Description is for illustrativepurposes only and is not meant to imply a required order, unlessspecified to require an order.

Embodiments may include various steps, which may be embodied inmachine-executable instructions to be executed by a general-purpose orspecial-purpose computer (or other electronic device). Alternatively,the steps may be performed by hardware components that include specificlogic for performing the steps or by a combination of hardware,software, and/or firmware.

Embodiments may also be provided as a computer program product includinga machine-readable medium having stored thereon instructions that may beused to program a computer (or other electronic device) to performprocesses described herein. The machine-readable medium may include, butis not limited to, hard drives, floppy diskettes, optical disks,CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs, EEPROMs, magnetic or opticalcards, solid-state memory devices, or other types ofmedia/machine-readable medium suitable for storing electronicinstructions.

As used herein, a software module or component may include any type ofcomputer instruction or computer executable code located within a memorydevice and/or transmitted as electronic signals over a system bus orwired or wireless network. A software module may, for instance, compriseone or more physical or logical blocks of computer instructions, whichmay be organized as a routine, program, object, component, datastructure, etc., that performs one or more tasks or implementsparticular abstract data types.

In certain embodiments, a particular software module may comprisedisparate instructions stored in different locations of a memory device,which together implement the described functionality of the module.Indeed, a module may comprise a single instruction or many instructions,and may be distributed over several different code segments, amongdifferent programs, and across several memory devices. Some embodimentsmay be practiced in a distributed computing environment where tasks areperformed by a remote processing device linked through a communicationsnetwork. In a distributed computing environment, software modules may belocated in local and/or remote memory storage devices. In addition, databeing tied or rendered together in a database record may be resident inthe same memory device, or across several memory devices, and may belinked together in fields of a record in a database across a network.

Although the invention has been described above with reference to thespecific embodiments thereof, it is not intended that the invention belimited to the above-mentioned embodiments. Various modifications andalternations may be made to the present invention without departing fromthe scope of the present invention. The scope of the present inventionis defined by the appended claims. Such words as “first” and “second”used in the description and the claims of the present invention aremerely illustrative and should by no means be considered as restrictive.

1. A method for automatic optimization of Doppler imaging parametersincluding at least one of a pulse repetition frequency and a baseline,the method comprising: obtaining at least two characteristic spectrallines at a predetermined pulse repetition frequency, wherein the atleast two characteristic spectral lines include a maximum power spectralline and a mean power spectral line; storing the at least twocharacteristic spectral lines; and optimizing at least one of theDoppler imaging parameters based on the stored at least twocharacteristic spectral lines and a predetermined mean noise power,wherein the at least two characteristic spectral lines are obtained bycollecting in real time Doppler spectral lines generated at thepredetermined pulse repetition frequency within a predetermined timeperiod, and processing the collected Doppler spectral lines in real timewithout storing them wherein optimizing the at least one of the Dopplerimaging parameters comprises: filtering out power values of the maximumpower spectral line and the mean power spectral line within a frequencycutoff range of wall filtering, thereby obtaining a new maximum powerspectral line and a new mean power spectral line; determining afrequency bin corresponding to a minimum power value of the new maximumpower spectral line; deciding whether or not a multi-aliasing occurs bycomparing a power value of the new mean power spectral linecorresponding to the determined frequency bin with a first thresholdvalue, wherein if the power value of the new mean power spectral linecorresponding to the determined frequency bin is larger than the firstthreshold value, it is determined that the multi-aliasing occurs;otherwise, no multi-aliasing occurs; searching for boundaries of a bloodflow signal based on the new maximum power spectral line and thepredetermined mean noise power if no multi-aliasing occurs, wherein theboundaries of the blood flow signal include both a positive frequencyboundary and a negative frequency boundary; adjusting the pulserepetition frequency down to a minimum value and the baseline to acenter of a display area if no boundaries of the blood flow signal arefound; and adjusting at least one of the Doppler imaging parametersbased on the boundaries of the blood flow signal if the boundaries ofthe blood flow signal are found.
 2. The method according to claim 1,wherein the predetermined pulse repetition frequency is selected fromone of a system-allowable maximum pulse repetition frequency, a maximumpulse repetition frequency at a current detection depth, or a maximumpulse repetition frequency under a current examination mode.
 3. Themethod according to claim 1, wherein obtaining the at least twocharacteristic spectral lines comprises: collecting in real time theDoppler spectral lines generated at the predetermined pulse repetitionfrequency; obtaining a final maximum power spectral line by selecting alarger power value in each frequency bin between a currently storedmaximum power spectral line and a currently collected Doppler spectralline within the predetermined time period; obtaining a total powerspectral line by accumulating in real time a power value in eachfrequency bin of the Doppler spectral lines collected in real timewithin the predetermined time period; and obtaining the mean powerspectral line by dividing the total power spectral line by the number ofthe Doppler spectral lines collected in real time within thepredetermined time period.
 4. The method of claim 1, wherein the firstthreshold value depends on the predetermined mean noise power.
 5. Themethod of claim 1, wherein searching for the boundaries of the bloodflow signal comprises: searching for the negative frequency boundary ofthe blood flow signal in the new maximum power spectral line, whichfurther comprises: searching from the determined frequency bin in apositive frequency direction; if the maximum power values in a number ofconsecutive frequency bins are larger than a second threshold value,determining that the negative frequency boundary of the blood flowsignal is found; and, if not, continuing searching in the positivefrequency direction until a maximum frequency bin and then searchingfrom a minimum frequency bin up to the determined frequency bin; andsearching for the positive frequency boundary of the blood flow signalin the new maximum power spectral line, which further comprises:searching from the determined frequency bin in a negative frequencydirection; if the maximum power values in a number of consecutivefrequency bins are larger than the second threshold value, determiningthat the positive frequency boundary of the blood flow signal is found;and, if not, continuing searching in the negative frequency directionuntil the minimum frequency bin and then searching from the maximumfrequency bin down to the determined frequency bin.
 6. The method ofclaim 5, wherein the second threshold value depends on the predeterminedmean noise power.
 7. The method according to claim 1, wherein adjustingat least one of the Doppler imaging parameters based on the boundariesof the blood flow signal comprises adjusting the pulse repetitionfrequency and the baseline based on the following equations:${P\; R\; F_{new}} = {{P\; R\;{F_{old}/\left( \frac{k*N}{N - \left( {f_{-} - f_{+}} \right)} \right)}} = {P\; R\;{F_{old}/L}}}$${baseline}_{new} = {{\frac{1 - k}{2}*N} + {\left( {{baseline}_{old} - f_{-}} \right)*L}}$where, PRF_(old) and PRF_(new) are the pulse repetition frequency andthe corresponding adjusted pulse repetition frequency, respectively;baseline_(old) and baseline_(new) are the baseline and the correspondingadjusted baseline, respectively; N is the number of frequency bins ofthe Doppler spectral lines; k is a predefined constant between 0 and 1,indicating a desired ratio of the frequency range of the blood flowsignal to an entire frequency space; f⁻ is the negative frequencyboundary of the blood flow signal; f₊ is the positive frequency boundaryof the blood flow signal; and L is a ratio of the pulse repetitionfrequency to the corresponding adjusted pulse repetition frequency. 8.The method according to claim 1, wherein the predetermined time periodis equal to at least one cardiac cycle.
 9. The method according to claim1, wherein the predetermined mean noise power is measured in advance atdifferent detection depths under different sampling gate widthconditions.
 10. A non-transitory computer-readable medium comprisingprogram code for causing a computer to perform a method for automaticoptimization of Doppler imaging parameters including at least one of apulse repetition frequency and a baseline, the method comprising:obtaining at least two characteristic spectral lines at a predeterminedpulse repetition frequency, wherein the at least two characteristicspectral lines include a maximum power spectral line and a mean powerspectral line; storing the at least two characteristic spectral lines;and optimizing at least one of the Doppler imaging parameters based onthe stored at least two characteristic spectral lines and apredetermined mean noise power, wherein the at least two characteristicspectral lines are obtained by collecting in real time the Dopplerspectral lines generated at the predetermined pulse repetition frequencywithin a predetermined time period, and processing the collected Dopplerspectral lines in real time without storing them wherein optimizing theat least one of the Doppler imaging parameters comprises: filtering outpower values of the maximum power spectral line and the mean powerspectral line within a frequency cutoff range of wall filtering, therebyobtaining a new maximum power spectral line and a new mean powerspectral line; determining a frequency bin corresponding to a minimumpower value of the new maximum power spectral line; deciding whether ornot a multi-aliasing occurs by comparing a power value of the new meanpower spectral line corresponding to the determined frequency bin with afirst threshold value, wherein if the power value of the new mean powerspectral line corresponding to the determined frequency bin is largerthan the first threshold value, it is determined that the multi-aliasingoccurs; otherwise, no multi-aliasing occurs; searching for boundaries ofa blood flow signal based on the new maximum power spectral line and thepredetermined mean noise power if no multi-aliasing occurs, wherein theboundaries of the blood flow signal include both a positive frequencyboundary and a negative frequency boundary; adjusting the pulserepetition frequency down to a minimum value and the baseline to acenter of a display area if no boundaries of the blood flow signal arefound; and adjusting at least one of the Doppler imaging parametersbased on the boundaries of the blood flow signal if the boundaries ofthe blood flow signal are found.